{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FC3C2ACFA91244878E10359A33959D7A",
    "mdEditEnable": false
   },
   "source": [
    "# 项目：TMDb电影数据分析报告\n",
    "\n",
    "## 目录\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"#intro\">简介</a></li>\n",
    "<li><a href=\"#wrangling\">数据整理</a></li>\n",
    "<li><a href=\"#eda\">探索性数据分析</a></li>\n",
    "<li><a href=\"#conclusions\">结论</a></li>\n",
    "</ul>\n",
    "\n",
    "<a id='intro'></a>\n",
    "\n",
    "## 简介\n",
    "TMDb电影数据集包含了 1960 年到 2015 年上映的大概一万部电影的信息，其中包括票房、电影名、演员、评分、电影风格等信息。对于电影的评分使用最多的就是TMDb评分。  \n",
    "要探索的问题有：\n",
    "1. 评分在哪个分数段的电影最多？\n",
    "2. 票房和评分之间有什么关系？\n",
    "3. 评分最高的一百部电影的主要风格是什么？\n",
    "4. 随着时间的推移，电影收入和数量是怎么变化的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "id": "7ED6FD6E8B764D138F435FADC72F1B02",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "# magic word\n",
    "%matplotlib inline\n",
    "%reload_ext autoreload\n",
    "# 使作图在jupyter notebook中能够显示出来，而不是通过弹出另外一个窗口来显示\n",
    "# 修改脚本后，不用手动重新导入，自动完成该操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "解决Matplotlib输出中文显示问题："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import mpl\n",
    "\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体\n",
    "mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "751FE3E230144E1A81CECC7777FC73E6",
    "mdEditEnable": false
   },
   "source": [
    "<a id='wrangling'></a>\n",
    "## 数据整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "id": "10B37B87A4734E978BD43185F78927F8",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# 加载数据并打印几行。进行这几项操作，来检查数据类型，以及是否有缺失数据或错误数据的情况。\n",
    "movies_df = pd.read_csv('tmdb-movies.csv')\n",
    "movies_df.info() # 检查数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在将数据导入以后，肯定要对数据进行理解，即知道各个数据所代表的含义。 数据字段含义如下：\n",
    "\n",
    "* imdb_id：IMDB 标识号\n",
    "* popularity：在 Movie Database 上的相对页面查看次数\n",
    "* budget：预算（美元）\n",
    "* revenue：收入（美元）\n",
    "* original_title：电影名称\n",
    "* cast：演员列表，按 | 分隔，最多 5 名演员\n",
    "* homepage：电影首页的 URL\n",
    "* director：导演列表，按 | 分隔，最多 5 名导演\n",
    "* tagline：电影的标语\n",
    "* keywords：与电影相关的关键字，按 | 分隔，最多 5 个关键字\n",
    "* overview：剧情摘要\n",
    "* runtime：电影时长\n",
    "* genres：风格列表，按 | 分隔，最多 5 种风格\n",
    "* production_companies：制作公司列表，按 | 分隔，最多 5 家公司\n",
    "* release_date：首次上映日期\n",
    "* vote_count：评分次数\n",
    "* vote_average：平均评分\n",
    "* release_year：发行年份\n",
    "* budget_adj：根据通货膨胀调整的预算（2010 年，美元）\n",
    "* revenue_adj：根据通货膨胀调整的收入（2010 年，美元）\n",
    "\n",
    "这些数据字段并不是每一个都是我们本次报告所需求的，在接下来的过程中，根据所要解决的问题，首先对这些数据进行一个清洗。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "92ED018B43EF4EA5BD9273F2F67309C2",
    "mdEditEnable": false
   },
   "source": [
    "**删除缺失值太多或对分析没有用的列**  \n",
    "**删除对分析结果无关的列（该报告主要分析与电影评分相关的内容以及电影数量和票房随时间的变化情况）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "id": "198D3B11D0724D04ACE4D611B995DB2C",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "drop_columns = ['imdb_id','id','overview','runtime','release_date','tagline','keywords',\n",
    "                'production_companies','budget_adj','revenue_adj','cast','homepage','director']\n",
    "movies_df.drop(drop_columns, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "12CEA9301B6F4E6A8FD583928E92E436",
    "mdEditEnable": false
   },
   "source": [
    "#### 删除重复行和含有缺失值的行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "id": "39D0859BEF9D44BB80559BF18AA9F712",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "# 删除重复行\n",
    "print(movies_df.duplicated().sum())\n",
    "movies_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "id": "8D55F81BF6A14B4E8AC5DE869C45147D",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10842, 8)\n"
     ]
    }
   ],
   "source": [
    "# 删除含有缺失值的行\n",
    "movies_df.dropna(inplace=True) # how='any'\n",
    "print(movies_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的缺失值处理不是很好。直接删除有缺失值的行，可能会引起大片输出的删除，丢失许多宝贵的信息。所以，建议要根据分析来进行缺失值的处理，比如说：如果C特征有大量缺失，但是只需要分析A特征与B特征的相互关系，那么就没必要对C特征进行缺失值的处理，只针对A特征跟B特征进行特征处理就足够了。同理其他特征也是一样。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3C0B9204ACE84AAF951A6E7386FE572D",
    "mdEditEnable": false,
    "scrolled": false
   },
   "source": [
    "#### 移动 vote_average 列到第一的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "id": "F61C44C3BE8A411789FE09F50A47B35C",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "vote_average = movies_df.pop('vote_average')\n",
    "movies_df.insert(0, 'vote_average', vote_average)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C0439263FFC1404EB117311BD5735397",
    "mdEditEnable": false
   },
   "source": [
    "#### 对 vote_average 进行降序排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "id": "7929999A67EE40178B80C26C98A76D6E"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.2</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Story of Film: An Odyssey</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>14</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   vote_average  popularity  budget  revenue                 original_title  \\\n",
       "0           9.2    0.006925       0        0  The Story of Film: An Odyssey   \n",
       "\n",
       "        genres  vote_count  release_year  \n",
       "0  Documentary          14          2011  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df = movies_df.sort_values(by=\"vote_average\", ascending=False)\n",
    "\n",
    "movies_df.index = np.arange(len(movies_df.index)) # 设置索引\n",
    "movies_df[:].iloc[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10842 entries, 0 to 10841\n",
      "Data columns (total 8 columns):\n",
      "vote_average      10842 non-null float64\n",
      "popularity        10842 non-null float64\n",
      "budget            10842 non-null int64\n",
      "revenue           10842 non-null int64\n",
      "original_title    10842 non-null object\n",
      "genres            10842 non-null object\n",
      "vote_count        10842 non-null int64\n",
      "release_year      10842 non-null int64\n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 762.3+ KB\n"
     ]
    }
   ],
   "source": [
    "movies_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## 探索性数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算统计值，创建可视化图表。采用系统化方法，一次只探索一个变量，然后探索变量之间的关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 评分在哪个分数段的电影最多？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HkjQmBp2s75SexfsARwynHEnSuBj0EFN67m8Gfm8ItUiSxsigYwkXAL+kGZzeG3j00CqS\nJI2FQQPiQuBQmj2JmZskaTc26CGmm6rqlUOtRJI0VgYNiI1JPgC8h+aiOarqU0OrSpI0coMGxC3A\n14Gj2uUCDAhJ2o0Neh3Euq72JGdX1R/Ob0mSpHGws1dEr5iPIiRJ42dnA8I5mSRpN+WcSpKkTjsb\nEF4PIUm7qUHPYiLJQ4CDaf550NVVtQX43WEVJkkarYH2INrZW9cBr6W5ovr9AFV13fBKkySN0qCH\nmI6sqicDN1bVR4B7DrEmSdIYGPQQ0/eT/AVwQJJTge8NsSZJu4npa6eZWjc1r33WmZ48uasMugdx\nCvBj4DJgKXf9/9SSpN3MoFdS/xx4y8xykmOAzw6rKEnS6A06SH1JX9Prh1CLJGmMbHcPIskRwK8D\nB/X829ElwC+GXZgkabRm24NIx8/rGeD6hyR7Jfloe3+fJOcluTzJ2WkM1DbnVyZJ2inb3YOoqsuB\ny5P8alX9w6CdJtkX+DxwWNt0MrC5qk5Ich5wHHD/Ads+vsOvSpK00wY9i+nlSf4oyZuTnJZktmD5\neVUdAWxum9YAn2jvXwJM7UCbJGkEUjX7OcVJ/hH4Ns1prkcDD66qkwfY7ttVdWiSC4E3VNVFSU6j\n+cdDKwZpq6pn9/W5FlgLsGzZspUbNmwY/NX22LJlC0uWLJnTtqMwSfVOUq0A191wHZu3bp59xTFw\nyOJDJqZWGE69K5evnNf+Zkza+3Zn6p2ampquqlWzrTfohXIregLhwiSf3sF6rqe5foL25/U0g92D\ntN1JVZ0FnAWwatWqWr169Q6W0ti0aRNz3XYUJqneSaoV4I3vfyOnf+v0UZcxkPWHrZ+YWmE49dZT\nh3Oh3KS9b3dFvYMeYro6yRlJ1iQ5g2bCvh1xMXB8e38NsHEH2iRJIzBoQHwM+CnwJOAGdvxK6vcC\nBye5ot3+4h1okySNwKCHmO5OM2B8C/BFYD/gl7NtVFWHtj+3Aif0PTxomyRpBAbag6iqt1XVE4FT\naQaSrxpiTZKkMTDQHkSSpwKPBu4FfJLm6mpJ0m5s0ENMy4GXV9XVwyxGkjQ+Bp3N9U3DLkSSNF4G\nPYtJkrTAGBCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkToPOxSSNXNZl\nKP2uP2z9UPqVJp17EJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQk\nqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROuyQg\nkhyVZHOSz7S3hyY5L8nlSc5OY5/+tl1RmySp267agzgAeEdVHVtVxwJHAZur6qHtY8cBJ3e0SZJG\nZM9d9DwHAE9O8jvANcAvgXPaxy4BpoAHAOf2tX18F9UnSeqTqhr+kyQrgftW1flJLgVWAo+rqouS\nnEazR7ECeENvW1U9u6OvtcBagGXLlq3csGHDnGrasmULS5YsmdsLGoFJqndYtU5fOz3vfQIcsvgQ\nNm/dPJS+59sk1QrDqXfl8pXz2t+MSfobg52rd2pqarqqVs223q7ag7gK+GrP/V8HlrbLS4HrgSUd\nbXdRVWcBZwGsWrWqVq9ePaeCNm3axFy3HYVJqndYtU6tm5r3PgHWH7ae0791+lD6nm+TVCsMp956\n6nC+1E7S3xjsmnp31RjEC4GnJNkDeAjwIuD49rE1wEbg4o42SdKI7KqAeBvwDODzwIeBvwEOTnIF\ncANNOLy3o02SNCK75BBTVV0LrO5rPqFveWtHmyRpRLxQTpLUyYCQJHUyICRJnQwISVInA0KS1GlX\nXSgnSfMi64Yzj+fGR3rpVT8DQvNu+trpoV31LGnX8RCTJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEh\nSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEh\nSerkvxxd4Ibx/33XH7Z+3vuUtOu5ByFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOnma64SYvnaa\nqXVToy5D2m0N62+szqx573NXcQ9CktTJgJAkdTIgJEmdxmoMIsk+wDnA/YArgFOqaqIO4A1j6gpw\n+gpJu95YBQRwMrC5qk5Ich5wHPDxYTyRg76StH0Zpy/oSd4HnFtV5yZ5IXDvqnpp3zprgbXt4uHA\nlXN8ugOB6+dc7K43SfVOUq0wWfVOUq0wWfVOUq2wc/U+oKruPdtK47YHcS/gx+39n9AEwJ1U1VnA\nWTv7REm+VFWrdrafXWWS6p2kWmGy6p2kWmGy6p2kWmHX1Dtug9TXA0vb+0uZrDSXpN3KuAXExcDx\n7f01wMYR1iJJC9q4BcR7gYOTXAHcQBMYw7LTh6l2sUmqd5Jqhcmqd5Jqhcmqd5JqhV1Q71gNUkuS\nxse47UFIksaEASFJ6rQgAyLJe5J8LslHkozbqb53kmTPJB9K8tkkfzvqegaR5IVJLhp1HbNJclSS\nzUk+097uclr1OEny5+379oIke4+6nu1Jsrrn93pNklNHXdO2JNk/yT+3f2OvH3U9s0lyQJJNbb0v\nH+ZzLbiASHIssGdVHQ3cnTvOmhpXTwAur6pjgOVJjhx1QduT5AHA2H4Y9DkAeEdVHdve5nrR5dAl\n+RXg19r37QXAISMuabuqatPM75Vm2pz/N+qatuMk4HPt39ivJfnVURc0i6cBX2vrPSbJA4f1RAsu\nIIDrgLe09yfh9X8MeFO7p3MPmgsIx9lbgJfOutZ4OAB4cpIvJDk3yXAm0pof/ws4IMmngN8E/mPE\n9QwkyX7AoVV1xahr2Y4bgSVJFgH7Ar8ccT2zCXC39v0aYGhfGifhA3JeVdW/VdUXkjwRuI0hzfU0\nX6pqS1X9DPgscF1VfWfUNW1LkqcBlwNfH3UtA/o28PKqejiwHHjkiOvZnnsDP6yq36LZezh2xPUM\n6jiGe7r6fPgw8Gjg34FvVNW/j7ie2fwjzZfFc4GtNKE2FAsuIACSPB54HnBiVd0y6nq2J8m9kiwG\nHkHzDXKcZxg8geab7gZgZZLnjrie2VwFXNRz/z4jq2R2P+GOece+Axw8wlp2xInAeaMuYhYvpTnU\nuAK4Z5JHjLieQTyrqp5EExA/GNaTLLiASHJf4M+AE6rqp6OuZwAvAn6vqm4FfsYQvy3srKp6WnvM\n+SnAdFW9bdQ1zeKFwFOS7AE8BPjqiOvZnmlgZt6dQ2lCYqy1h0BWA5eMuJTZ3A34RXt/K7BkhLUM\n4reAd7ZfHI8EPjesJ1pwAUEzgLocuLA9w+KZoy5oFm8HnpnkMuC/gAtHXM/u5G3AM4DPAx+uqrE9\nNFZVlwH/leSLwJVV9YVR1zSAo4CvV9UvZl1ztN4OPKf9G9uX8T8kdgGwD/Bp4JVVtWVYT+SV1JKk\nTgtxD0KSNAADQpLUyYCQJHUyIKRWkiPH/Up1aVcyIKQ7HMkQr0qVJo0Bod1ekhclOam9/3+SnJrk\n/e1kZ+9LsneS1wEvA16WZFO77n5JzmnXe/t2+l+e5NNJLkvy6rbtI0kOae+fk+T+SQ5rJ1n7UpJT\n2seenuSMJBcm+eO27Q+SXN5OzHd02/aCJF9uJ+r7cFvzs5N8vn3u+w3xV6gFyoDQQnAOzVQK0Fzp\nfQ+a8/OPAf4NeEZVvRh4DfCaqlrdrrsW+GrPRIlHbKP/+wFn0Ez8+Pi27Vzg0Un2Au5eVVcDrwde\nQXNV/It75n56KvCHVfXX7fJSmvmW1nHHxIe/Dzwc2Ay8qn0NfwocA7wBeMEO/k6kWY31VNfSfKiq\n77ZTluwP3AIcDvxT+/BlwGO2senhwCOSrKb5QD6YZmbSfltpAuIm7rgK95+Bd9DM7/OJnv7WAQUs\navsEeGdV9U6XsB/wPpp/u3tb23YF8Ema+aOuAB4G3ItmqpA9uWMaDmneGBBaKD5JM8XK+TQfwEfT\nfLgeDXytXefnwIFw+zQRVwJfqKq/S/I7wDXb6Pt0mr2Dy2kDpKpubPs4EXhru96VwAuq6j+SPJ87\nZg29/UrYdo/jOVV1eJLHAb/XTgVyr3ZPZma97wD/WlWPSnJ/miktpHllQGih+BDwFWAFzTf9v0/y\nWeC7NIeWoPmmf06Sk4E/B97drvdHwI9oDgV1+Wi77veAm5IcVFX/STNT8DOramZq7pcAf5Pk7sAn\nq+qm/hnGq+rmJFe0U2p8Cziwqm5LckiSS4GfAudU1bvbKcovBRbThJ80r5xqQxpzSQ6mCbhf0vzv\ngm9W1UtGW5UWAgNCGlA7E/A5fc3fraqTRlGPNGwGhCSpk6e5SpI6GRCSpE4GhCSpkwEhSepkQEiS\nOhkQkqRO/x+ZbVWOwh83uQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4077cb83c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [2,3,4,5,6,7,8,9]\n",
    "bins = np.linspace(2, 9, 15, endpoint=True)\n",
    "plt.title('电影评分分布图', fontsize='15')\n",
    "plt.xlabel('vote_average')\n",
    "plt.ylabel('vote_number')\n",
    "plt.hist(movies_df['vote_average'], color='green', bins=bins);\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由直方图可以看出大多数的电影评分都在5.5~7.0分之间，呈左偏态。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 票房和评分之间有什么关系？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除票房为0的列\n",
    "revenue_df = movies_df[movies_df['revenue']!=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评分是连续的值，所以需要把评分分组，创建类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用于把数据“分割”成组的边缘数据\n",
    "bin_edges = np.linspace(2, 8.5, 14, endpoint=True)\n",
    "# 对每个评分水平类别进行标签命名\n",
    "bin_names = np.linspace(2, 8, 13, endpoint=True).astype(str)\n",
    "# pandas.cut()根据这些值将该列进行拆分，创建 vote_levels 列\n",
    "vote_levels = pd.cut(revenue_df['vote_average'], bin_edges, labels=bin_names, right=False)\n",
    "revenue_df.insert(len(revenue_df.columns), 'vote_levels', vote_levels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：  \n",
    "> revenue_df['vote_levels'] = pd.cut(revenue_df['vote_average'], bin_edges, labels=bin_names, right=False)  \n",
    "会报警告：  \n",
    "/opt/conda/lib/python3.5/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning:  \n",
    "A value is trying to be set on a copy of a slice from a DataFrame.  \n",
    "Try using .loc[row_indexer,col_indexer] = value instead  \n",
    "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy  \n",
    "  \"\"\"Entry point for launching an IPython kernel.  \n",
    "**修正方案**：  \n",
    "先生成好正确的数组再插入dataframe中，如上一个cell所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0    1204\n",
       "6.5    1007\n",
       "5.5     979\n",
       "7.0     553\n",
       "5.0     552\n",
       "4.5     232\n",
       "7.5     185\n",
       "4.0      68\n",
       "3.5      29\n",
       "8.0      28\n",
       "3.0      10\n",
       "2.0       2\n",
       "2.5       0\n",
       "Name: vote_levels, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "revenue_df['vote_levels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据vote_levels 列进行分组并求平均值\n",
    "revenue_mean = revenue_df.groupby('vote_levels')['revenue'].mean()\n",
    "vote_levels = np.linspace(2, 8, 13, endpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LQ0NpGx9P4xj6+mrv2il0Xa1dO3WAmE7XVQ4eIW1mVg89PTBnTt36/BvedTUFtxzMzNpV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\ng4OZmZVxcDAzszIODmZmVub/ByQinvU2mVSKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f406f975438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('vote_levels')\n",
    "plt.ylabel('revenue_mean')\n",
    "plt.title(u'评分和票房关系图', fontsize='15')\n",
    "plt.grid(True)\n",
    "plt.scatter(vote_levels, revenue_mean, c='r', s=200, alpha=0.7);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从散点图可以看出，评分和票房是呈正相关的关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 评分最高的一百部电影的主要风格是什么？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>genres</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.2</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Story of Film: An Odyssey</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>14</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.9</td>\n",
       "      <td>0.114264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>The Mask You Live In</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>11</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.8</td>\n",
       "      <td>0.222293</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Life Cycles</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>27</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vote_average  popularity  budget  revenue                 original_title  \\\n",
       "0           9.2    0.006925       0        0  The Story of Film: An Odyssey   \n",
       "1           8.9    0.114264       0        0           The Mask You Live In   \n",
       "2           8.8    0.222293       0        0                    Life Cycles   \n",
       "\n",
       "        genres  vote_count  release_year  \n",
       "0  Documentary          14          2011  \n",
       "1  Documentary          11          2015  \n",
       "2  Documentary          27          2010  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vote_ranking = movies_df.iloc[:100]\n",
    "vote_ranking.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 得到包含所有电影风格的列表\n",
    "\n",
    "'''这是之前的写法\n",
    "genres_list = [] \n",
    "for i in vote_ranking['genres']:\n",
    "    genres_list = genres_list + i.split('|')\n",
    "'''\n",
    "\n",
    "from functools import reduce\n",
    "genres_list = vote_ranking['genres'].apply(lambda x: x.split('|'))\n",
    "genres = pd.Series(reduce(lambda x,y: x+y, genres_list)) # 迭代列表求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前十五种风格\n",
    "genres_counts = genres.value_counts().iloc[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Tn07zvGV2lGS3Xo3YpLDorvPE8lO9KmzfDNYf/gfAZdHvXckCTgZmYxSEjG62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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f406ebf0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.title('电影主要风格图', fontsize='15')\n",
    "labels = genres_counts.index\n",
    "plt.pie(genres_counts.values, labels = labels, autopct='%1.1f%%');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f406d964f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 横向柱状图\n",
    "plt.barh(genres_counts.index[::-1], genres_counts.values[::-1]); # [::-1]表示反向排序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "高评分电影的主要风格有：Drama, Documentary, Music, Crime, Comedy。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**制作电影风格的词云图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba                      #分词库\n",
    "from wordcloud import WordCloud   #词云库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将各电影的风格以‘/’分隔，形成字符串\n",
    "\n",
    "'''这是之前的写法\n",
    "genres_str = ''\n",
    "for i in genres.astype(str):\n",
    "    genres_str = genres_str + i + '/'\n",
    "genres_str = genres_str[:-1]\n",
    "'''\n",
    "\n",
    "genres_str = \"/\".join(genres.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RDtPRO+GziGOs8RpekV6cHTul0+uWGbNlxnWNNQVT8ZTgdcenW+N+bn8Yk5Lk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\nTyAQCGMAIvYEAoEwBiBiTyAQCGOA/wdL8gUqC3UGiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4077cee780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对分词后的文本生成词云\n",
    "genres_wordcloud = WordCloud().generate(genres_str)\n",
    "plt.title('电影风格云图', fontsize='15')\n",
    "plt.imshow(genres_wordcloud)\n",
    "plt.axis(\"off\"); #去掉x,y轴的刻度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、随着时间的推移，电影收入和数量是怎么变化的？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 移除收入为0的列\n",
    "revenue_df = movies_df[movies_df['revenue']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计每个年份的电影数量，并按照年份排序\n",
    "movie_num = revenue_df['release_year'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计每个年份的电影收入\n",
    "movie_revenue = revenue_df.groupby('release_year').sum()['revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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unHEnT817ii/v+WUuOOiC+nvQmBlDew1laK+hrK9cz6SFk3h2wbP88fU/AvD1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\nERHJOEpwREREJOP8H8w3c06eiII8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4077de4860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.grid(True)\n",
    "plt.ylabel('revenue')\n",
    "plt.xlabel('release_year')\n",
    "plt.title('电影票房随时间的走势图', fontsize='14')\n",
    "plt.plot(movie_revenue, color='green', alpha=.7)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.grid(True)\n",
    "plt.ylabel('number')\n",
    "plt.xlabel('release_year')\n",
    "plt.title('电影数量随时间的走势图', fontsize='14')\n",
    "plt.plot(movie_num, color='red', alpha=.7);\n",
    "\n",
    "plt.tight_layout() # 调整子图之间的距离"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随着时间的推移，电影事业得到了巨大的发展，电影数量和收入都大幅增加，而且是同步增加。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## 结论\n",
    "\n",
    "> 总结调查结果。请确保你了解在探索过程中的局限性。如果尚未进行任何统计检验，不要做出任何统计结论。切记不要根据相关关系贸然推导出因果关系！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过对该电影数据集的分析，可以得到以下结论：\n",
    "1. 随着时间的推移，电影获得了巨大发展，尤其是1990年之后，电影数量和票房增加迅速。\n",
    "2. 电影评分和票房呈正相关关系，说明电影质量高，观众是愿意为其买单的。\n",
    "3. 大多数电影的评分主要分布在5.5~7.0分之间，比较明显的正态分布。\n",
    "4. 高评分的电影风格主要有Drama、Documentary、Music等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 局限性：\n",
    "1. 某些电影的评分次数太少（小于50），这使得电影评分很有可能不能真实反映出电影的质量水平；\n",
    "2. 电影收入为零的无效数据太多（占一半多），降低了最后结果的可靠性；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考：\n",
    "1. [IMDB Movie :Python数据分析报告](https://zhuanlan.zhihu.com/p/34034272)  \n",
    "2. [IMDB电影数据分析](https://www.kesci.com/apps/home/project/5ad5a5687238515d80b55cba/code)  \n",
    "3. [TMDB电影数据分析](https://zhuanlan.zhihu.com/p/35164262)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
